1. Field of Invention
The present invention relates to modeling space-time data, and more particularly to detecting three-dimensional convex clusters in space and time for a phenomenon.
2. Discussion of Related Art
Detection of clusters in space and time, called space-time clusters, is an important function in various domains. For example, detection of such clusters is an important part of the investigation of disease outbreaks in the domain of epidemiology and public health. Other domains of application include medical imaging, urban planning and reconnaissance. The notion of what constitutes a cluster depends on the domain. For example, the spatial scan statistic as described in the paper “A spatial scan statistic” by Martin Kulldorff in Communications in Statistics; Theory and Methods, Volume 26, Number 6, 1997, is widely used in the epidemiology and public health domain. Other models of clustering might be appropriate in other domains.
Methods for detecting clusters may be developed depending on the clustering notion used. For example, the use of the scan statistic implies that earlier hierarchical approaches (see for example, “Automatic subspace clustering of high dimensional data for data mining applications” by R. Agrawal, J. Gehrke, D. Gunopuios, and P. Raghavan in Proceedings of the ACM-SIGMOD International Conference on Management of Data, 1998) to clustering cannot be applied. An example of a system that may handle the spatial scan statistic model for clustering is the SaTScan system. SaTScan may be used to detect space-time clusters with a cylindrical shape, representing a circular region in space for the entire duration of an interval in time.